class_docstr: Calculate mean AP for object detection task
  
  Parameters:
  ---------
  iou_thresh : float
      IOU overlap threshold for TP
  class_names : list of str
      optional, if provided, will print out AP for each class
class_docstr_tok: ["Calculate","mean","AP","for","object","detection","task","Parameters",":","--","--","--","--","-","iou_thresh",":","float","IOU","overlap","threshold","for","TP","class_names",":","list","of","str","optional",",","if","provided",",","will","print","out","AP","for","each","class"]
docstr: Clear the internal statistics to initial state.
docstr_tok: ["Clear","the","internal","statistics","to","initial","state","."]
code: def reset(self):
      """Clear the internal statistics to initial state."""
      if getattr(self, "num", None) is None:
          self.num_inst = 0
          self.sum_metric = 0.0
      else:
          self.num_inst = [0] * self.num
          self.sum_metric = [0.0] * self.num
      self._n_pos = defaultdict(int)
      self._score = defaultdict(list)
      self._match = defaultdict(list)
  
file_path: gluon-cv/gluoncv/utils/metrics/voc_detection.py
imports: from __future__ import division
  from collections import defaultdict
  import numpy
  import mxnet
  from ..bbox import bbox_iou
label_desc: Use image node_id 2 for calling the VOCMApMetric obj's underlying member method code with example usage: obj.reset() without return types